Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations1033
Missing cells166
Missing cells (%)0.7%
Duplicate rows4
Duplicate rows (%)0.4%
Total size in memory193.8 KiB
Average record size in memory192.1 B

Variable types

Text1
Numeric14
Categorical9

Alerts

Dataset has 4 (0.4%) duplicate rowsDuplicates
budget is highly overall correlated with costo_clicks and 4 other fieldsHigh correlation
budget_categoria is highly overall correlated with roiHigh correlation
conversion_categoria is highly overall correlated with conversion_rate and 1 other fieldsHigh correlation
conversion_rate is highly overall correlated with conversion_categoria and 2 other fieldsHigh correlation
conversions is highly overall correlated with conversion_rate and 6 other fieldsHigh correlation
costo_clicks is highly overall correlated with budget and 1 other fieldsHigh correlation
costo_por_conversion is highly overall correlated with budget and 6 other fieldsHigh correlation
efficiency_index is highly overall correlated with conversion_categoria and 2 other fieldsHigh correlation
ingresos_por_click is highly overall correlated with budget and 6 other fieldsHigh correlation
net_profit is highly overall correlated with conversions and 5 other fieldsHigh correlation
revenue is highly overall correlated with conversions and 6 other fieldsHigh correlation
revenue_categoria is highly overall correlated with conversions and 1 other fieldsHigh correlation
revenue_per_dollar is highly overall correlated with budget and 6 other fieldsHigh correlation
roi is highly overall correlated with budget_categoria and 2 other fieldsHigh correlation
roi_categoria is highly overall correlated with roiHigh correlation
roi_recalculated is highly overall correlated with budget and 6 other fieldsHigh correlation
start_month is highly overall correlated with temporada_inicioHigh correlation
target_audience is highly overall correlated with typeHigh correlation
temporada_inicio is highly overall correlated with start_monthHigh correlation
type is highly overall correlated with target_audienceHigh correlation
roi_recalculated has 22 (2.1%) missing values Missing
costo_por_conversion has 19 (1.8%) missing values Missing
costo_clicks has 24 (2.3%) missing values Missing
ingresos_por_click has 35 (3.4%) missing values Missing
budget is highly skewed (γ1 = 31.66435554) Skewed
costo_por_conversion is highly skewed (γ1 = 22.68423989) Skewed
revenue_categoria is uniformly distributed Uniform
conversion_rate has 11 (1.1%) zeros Zeros
conversions has 11 (1.1%) zeros Zeros
costo_clicks has 11 (1.1%) zeros Zeros
efficiency_index has 17 (1.6%) zeros Zeros

Reproduction

Analysis started2025-05-25 08:32:12.275417
Analysis finished2025-05-25 08:32:34.080796
Duration21.81 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct1014
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:34.312766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length53
Median length45
Mean length33.403679
Min length11

Characters and Unicode

Total characters34506
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1001 ?
Unique (%)96.9%

Sample

1st rowPublic-key multi-tasking throughput
2nd rowDe-engineered analyzing task-force
3rd rowBalanced solution-oriented Local Area Network
4th rowDistributed real-time methodology
5th rowFront-line executive infrastructure
ValueCountFrequency (%)
interface 41
 
1.2%
architecture 30
 
0.9%
open 28
 
0.8%
zero 23
 
0.7%
secured 23
 
0.7%
user 22
 
0.7%
4thgeneration 21
 
0.6%
methodology 19
 
0.6%
local 19
 
0.6%
reverse-engineered 19
 
0.6%
Other values (335) 3063
92.6%
2025-05-25T10:32:34.623766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

budget
Real number (ℝ)

High correlation  Skewed 

Distinct1009
Distinct (%)98.1%
Missing4
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean58955.861
Minimum-10000
Maximum9999999
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T10:32:34.710685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10000
5-th percentile5686.904
Q124633.17
median46919.95
Q374898.2
95-th percentile95260.468
Maximum9999999
Range10009999
Interquartile range (IQR)50265.03

Descriptive statistics

Standard deviation311545.96
Coefficient of variation (CV)5.2843934
Kurtosis1011.3254
Mean58955.861
Median Absolute Deviation (MAD)25128.49
Skewness31.664356
Sum60665581
Variance9.7060888 × 1010
MonotonicityNot monotonic
2025-05-25T10:32:34.825058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8082.3 4
 
0.4%
84643.1 3
 
0.3%
17712.98 3
 
0.3%
14589.75 3
 
0.3%
36800.58 3
 
0.3%
59892.4 2
 
0.2%
39291.9 2
 
0.2%
75569.28 2
 
0.2%
28964.45 2
 
0.2%
40493.88 2
 
0.2%
Other values (999) 1003
97.1%
(Missing) 4
 
0.4%
ValueCountFrequency (%)
-10000 1
0.1%
1052.57 1
0.1%
1223.82 1
0.1%
1309.17 1
0.1%
1378.61 1
0.1%
1380.68 1
0.1%
1407.21 1
0.1%
1436.99 1
0.1%
1480.67 1
0.1%
1580.69 1
0.1%
ValueCountFrequency (%)
9999999 1
0.1%
100000 1
0.1%
99957.15 1
0.1%
99891.35 1
0.1%
99838.63 1
0.1%
99714.19 1
0.1%
99579.39 1
0.1%
99535.21 1
0.1%
99520.93 1
0.1%
99406.41 1
0.1%

roi
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)8.9%
Missing4
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.53400389
Minimum-0.2
Maximum0.99
Zeros6
Zeros (%)0.6%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T10:32:34.934815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.2
5-th percentile0.134
Q10.31
median0.53
Q30.76
95-th percentile0.95
Maximum0.99
Range1.19
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.26182074
Coefficient of variation (CV)0.49029745
Kurtosis-1.1164625
Mean0.53400389
Median Absolute Deviation (MAD)0.23
Skewness0.0059019795
Sum549.49
Variance0.068550101
MonotonicityNot monotonic
2025-05-25T10:32:35.562719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 33
 
3.2%
0.4 29
 
2.8%
0.5 28
 
2.7%
0.6 27
 
2.6%
0.3 22
 
2.1%
0.74 22
 
2.1%
0.16 18
 
1.7%
0.8 17
 
1.6%
0.37 16
 
1.5%
0.1 16
 
1.5%
Other values (82) 801
77.5%
ValueCountFrequency (%)
-0.2 1
 
0.1%
0 6
 
0.6%
0.1 16
1.5%
0.11 6
 
0.6%
0.12 11
1.1%
0.13 12
1.2%
0.14 10
1.0%
0.15 12
1.2%
0.16 18
1.7%
0.17 10
1.0%
ValueCountFrequency (%)
0.99 13
 
1.3%
0.98 8
 
0.8%
0.97 11
 
1.1%
0.96 10
 
1.0%
0.95 11
 
1.1%
0.94 13
 
1.3%
0.93 8
 
0.8%
0.92 6
 
0.6%
0.91 9
 
0.9%
0.9 33
3.2%

type
Categorical

High correlation 

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size8.2 KiB
email
289 
webinar
268 
social media
240 
podcast
233 
event
 
1

Length

Max length12
Median length7
Mean length7.5968992
Min length3

Characters and Unicode

Total characters7840
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowemail
2nd rowemail
3rd rowpodcast
4th rowwebinar
5th rowsocial media

Common Values

ValueCountFrequency (%)
email 289
28.0%
webinar 268
25.9%
social media 240
23.2%
podcast 233
22.6%
event 1
 
0.1%
B2B 1
 
0.1%
(Missing) 1
 
0.1%

Length

2025-05-25T10:32:35.662703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:35.727222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
email 289
22.7%
webinar 268
21.1%
social 240
18.9%
media 240
18.9%
podcast 233
18.3%
event 1
 
0.1%
b2b 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1270
16.2%
i 1037
13.2%
e 799
10.2%
m 529
 
6.7%
l 529
 
6.7%
s 473
 
6.0%
o 473
 
6.0%
d 473
 
6.0%
c 473
 
6.0%
n 269
 
3.4%
Other values (9) 1515
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1270
16.2%
i 1037
13.2%
e 799
10.2%
m 529
 
6.7%
l 529
 
6.7%
s 473
 
6.0%
o 473
 
6.0%
d 473
 
6.0%
c 473
 
6.0%
n 269
 
3.4%
Other values (9) 1515
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1270
16.2%
i 1037
13.2%
e 799
10.2%
m 529
 
6.7%
l 529
 
6.7%
s 473
 
6.0%
o 473
 
6.0%
d 473
 
6.0%
c 473
 
6.0%
n 269
 
3.4%
Other values (9) 1515
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1270
16.2%
i 1037
13.2%
e 799
10.2%
m 529
 
6.7%
l 529
 
6.7%
s 473
 
6.0%
o 473
 
6.0%
d 473
 
6.0%
c 473
 
6.0%
n 269
 
3.4%
Other values (9) 1515
19.3%

target_audience
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size8.2 KiB
B2B
529 
B2C
501 
social media
 
1

Length

Max length12
Median length3
Mean length3.0087294
Min length3

Characters and Unicode

Total characters3102
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowB2B
2nd rowB2C
3rd rowB2B
4th rowB2B
5th rowB2B

Common Values

ValueCountFrequency (%)
B2B 529
51.2%
B2C 501
48.5%
social media 1
 
0.1%
(Missing) 2
 
0.2%

Length

2025-05-25T10:32:35.824524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:35.874993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
b2b 529
51.3%
b2c 501
48.5%
social 1
 
0.1%
media 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 1559
50.3%
2 1030
33.2%
C 501
 
16.2%
a 2
 
0.1%
i 2
 
0.1%
o 1
 
< 0.1%
s 1
 
< 0.1%
c 1
 
< 0.1%
l 1
 
< 0.1%
1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1559
50.3%
2 1030
33.2%
C 501
 
16.2%
a 2
 
0.1%
i 2
 
0.1%
o 1
 
< 0.1%
s 1
 
< 0.1%
c 1
 
< 0.1%
l 1
 
< 0.1%
1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1559
50.3%
2 1030
33.2%
C 501
 
16.2%
a 2
 
0.1%
i 2
 
0.1%
o 1
 
< 0.1%
s 1
 
< 0.1%
c 1
 
< 0.1%
l 1
 
< 0.1%
1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1559
50.3%
2 1030
33.2%
C 501
 
16.2%
a 2
 
0.1%
i 2
 
0.1%
o 1
 
< 0.1%
s 1
 
< 0.1%
c 1
 
< 0.1%
l 1
 
< 0.1%
1
 
< 0.1%
Other values (3) 3
 
0.1%

channel
Categorical

Distinct4
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size8.2 KiB
promotion
281 
referral
258 
organic
250 
paid
243 

Length

Max length9
Median length8
Mean length7.0881783
Min length4

Characters and Unicode

Total characters7315
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roworganic
2nd rowpromotion
3rd rowpaid
4th roworganic
5th rowpromotion

Common Values

ValueCountFrequency (%)
promotion 281
27.2%
referral 258
25.0%
organic 250
24.2%
paid 243
23.5%
(Missing) 1
 
0.1%

Length

2025-05-25T10:32:35.952818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:36.012822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
promotion 281
27.2%
referral 258
25.0%
organic 250
24.2%
paid 243
23.5%

Most occurring characters

ValueCountFrequency (%)
r 1305
17.8%
o 1093
14.9%
i 774
10.6%
a 751
10.3%
n 531
7.3%
p 524
7.2%
e 516
 
7.1%
m 281
 
3.8%
t 281
 
3.8%
f 258
 
3.5%
Other values (4) 1001
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1305
17.8%
o 1093
14.9%
i 774
10.6%
a 751
10.3%
n 531
7.3%
p 524
7.2%
e 516
 
7.1%
m 281
 
3.8%
t 281
 
3.8%
f 258
 
3.5%
Other values (4) 1001
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1305
17.8%
o 1093
14.9%
i 774
10.6%
a 751
10.3%
n 531
7.3%
p 524
7.2%
e 516
 
7.1%
m 281
 
3.8%
t 281
 
3.8%
f 258
 
3.5%
Other values (4) 1001
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1305
17.8%
o 1093
14.9%
i 774
10.6%
a 751
10.3%
n 531
7.3%
p 524
7.2%
e 516
 
7.1%
m 281
 
3.8%
t 281
 
3.8%
f 258
 
3.5%
Other values (4) 1001
13.7%

conversion_rate
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)8.9%
Missing4
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.54205053
Minimum0
Maximum1.5
Zeros11
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:36.112831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.3
median0.55
Q30.77
95-th percentile0.95
Maximum1.5
Range1.5
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.26724814
Coefficient of variation (CV)0.49303178
Kurtosis-1.0556927
Mean0.54205053
Median Absolute Deviation (MAD)0.23
Skewness-0.025238534
Sum557.77
Variance0.071421569
MonotonicityNot monotonic
2025-05-25T10:32:36.228488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 27
 
2.6%
0.7 27
 
2.6%
0.2 24
 
2.3%
0.9 21
 
2.0%
0.8 19
 
1.8%
0.1 18
 
1.7%
0.6 18
 
1.7%
0.3 17
 
1.6%
0.85 17
 
1.6%
0.65 17
 
1.6%
Other values (82) 824
79.8%
ValueCountFrequency (%)
0 11
1.1%
0.1 18
1.7%
0.11 6
 
0.6%
0.12 13
1.3%
0.13 12
1.2%
0.14 5
 
0.5%
0.15 6
 
0.6%
0.16 11
1.1%
0.17 10
1.0%
0.18 14
1.4%
ValueCountFrequency (%)
1.5 1
 
0.1%
0.99 13
1.3%
0.98 10
1.0%
0.97 13
1.3%
0.96 12
1.2%
0.95 10
1.0%
0.94 8
0.8%
0.93 6
0.6%
0.92 12
1.2%
0.91 9
0.9%

revenue
Real number (ℝ)

High correlation 

Distinct1008
Distinct (%)97.9%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean511596.07
Minimum108.21
Maximum999712.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:36.338349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum108.21
5-th percentile50790.725
Q1267827.05
median517944.04
Q3765478.94
95-th percentile949543.24
Maximum999712.49
Range999604.28
Interquartile range (IQR)497651.89

Descriptive statistics

Standard deviation287153.14
Coefficient of variation (CV)0.5612888
Kurtosis-1.1814039
Mean511596.07
Median Absolute Deviation (MAD)249792.12
Skewness-0.047754757
Sum5.2694395 × 108
Variance8.2456926 × 1010
MonotonicityNot monotonic
2025-05-25T10:32:36.442728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709593.48 4
 
0.4%
516609.1 3
 
0.3%
458227.42 3
 
0.3%
89958.73 3
 
0.3%
558302.11 3
 
0.3%
206241.46 3
 
0.3%
174462.47 2
 
0.2%
47511.35 2
 
0.2%
172882.59 2
 
0.2%
734755.76 2
 
0.2%
Other values (998) 1003
97.1%
(Missing) 3
 
0.3%
ValueCountFrequency (%)
108.21 1
0.1%
2810.51 1
0.1%
3641.3 1
0.1%
4190.95 1
0.1%
5971.96 1
0.1%
7622.28 1
0.1%
7636.54 1
0.1%
8272.5 1
0.1%
8811.92 1
0.1%
9576.39 1
0.1%
ValueCountFrequency (%)
999712.49 1
0.1%
999317.92 1
0.1%
997657.18 1
0.1%
996578.25 1
0.1%
996493.1 1
0.1%
995340.62 1
0.1%
994306.41 1
0.1%
993906.77 1
0.1%
993317.73 1
0.1%
992544.71 1
0.1%

duracion_dias
Real number (ℝ)

Distinct498
Distinct (%)48.4%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean364.66634
Minimum-60
Maximum716
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T10:32:36.551953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile111.7
Q1253
median363.5
Q3474.25
95-th percentile617.65
Maximum716
Range776
Interquartile range (IQR)221.25

Descriptive statistics

Standard deviation152.77389
Coefficient of variation (CV)0.41894156
Kurtosis-0.58228402
Mean364.66634
Median Absolute Deviation (MAD)110.5
Skewness0.0099787088
Sum374877
Variance23339.86
MonotonicityNot monotonic
2025-05-25T10:32:36.660546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
339 7
 
0.7%
441 7
 
0.7%
366 6
 
0.6%
423 6
 
0.6%
295 6
 
0.6%
328 6
 
0.6%
348 6
 
0.6%
314 6
 
0.6%
321 5
 
0.5%
275 5
 
0.5%
Other values (488) 968
93.7%
ValueCountFrequency (%)
-60 1
 
0.1%
10 1
 
0.1%
16 2
0.2%
22 3
0.3%
24 1
 
0.1%
31 1
 
0.1%
32 1
 
0.1%
34 1
 
0.1%
36 1
 
0.1%
41 1
 
0.1%
ValueCountFrequency (%)
716 2
0.2%
712 1
0.1%
707 1
0.1%
700 1
0.1%
698 1
0.1%
693 1
0.1%
689 1
0.1%
688 2
0.2%
687 2
0.2%
683 1
0.1%

roi_categoria
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing4
Missing (%)0.4%
Memory size8.2 KiB
Medio
543 
Bajo
479 
Pérdida
 
7

Length

Max length7
Median length5
Mean length4.548105
Min length4

Characters and Unicode

Total characters4680
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBajo
2nd rowMedio
3rd rowBajo
4th rowBajo
5th rowBajo

Common Values

ValueCountFrequency (%)
Medio 543
52.6%
Bajo 479
46.4%
Pérdida 7
 
0.7%
(Missing) 4
 
0.4%

Length

2025-05-25T10:32:36.766938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:36.827647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medio 543
52.8%
bajo 479
46.6%
pérdida 7
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 1022
21.8%
d 557
11.9%
i 550
11.8%
e 543
11.6%
M 543
11.6%
a 486
10.4%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1022
21.8%
d 557
11.9%
i 550
11.8%
e 543
11.6%
M 543
11.6%
a 486
10.4%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1022
21.8%
d 557
11.9%
i 550
11.8%
e 543
11.6%
M 543
11.6%
a 486
10.4%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1022
21.8%
d 557
11.9%
i 550
11.8%
e 543
11.6%
M 543
11.6%
a 486
10.4%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

conversion_categoria
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing4
Missing (%)0.4%
Memory size8.2 KiB
Baja
367 
Media
332 
Alta
330 

Length

Max length5
Median length4
Mean length4.3226433
Min length4

Characters and Unicode

Total characters4448
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaja
2nd rowMedia
3rd rowBaja
4th rowBaja
5th rowAlta

Common Values

ValueCountFrequency (%)
Baja 367
35.5%
Media 332
32.1%
Alta 330
31.9%
(Missing) 4
 
0.4%

Length

2025-05-25T10:32:36.911884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:36.971847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
baja 367
35.7%
media 332
32.3%
alta 330
32.1%

Most occurring characters

ValueCountFrequency (%)
a 1396
31.4%
B 367
 
8.3%
j 367
 
8.3%
M 332
 
7.5%
e 332
 
7.5%
d 332
 
7.5%
i 332
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1396
31.4%
B 367
 
8.3%
j 367
 
8.3%
M 332
 
7.5%
e 332
 
7.5%
d 332
 
7.5%
i 332
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1396
31.4%
B 367
 
8.3%
j 367
 
8.3%
M 332
 
7.5%
e 332
 
7.5%
d 332
 
7.5%
i 332
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1396
31.4%
B 367
 
8.3%
j 367
 
8.3%
M 332
 
7.5%
e 332
 
7.5%
d 332
 
7.5%
i 332
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

revenue_categoria
Categorical

High correlation  Uniform 

Distinct3
Distinct (%)0.3%
Missing3
Missing (%)0.3%
Memory size8.2 KiB
Media
344 
Mucha
343 
Poca
343 

Length

Max length5
Median length5
Mean length4.6669903
Min length4

Characters and Unicode

Total characters4807
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMucha
2nd rowMedia
3rd rowMedia
4th rowPoca
5th rowPoca

Common Values

ValueCountFrequency (%)
Media 344
33.3%
Mucha 343
33.2%
Poca 343
33.2%
(Missing) 3
 
0.3%

Length

2025-05-25T10:32:37.053019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:37.112894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
media 344
33.4%
mucha 343
33.3%
poca 343
33.3%

Most occurring characters

ValueCountFrequency (%)
a 1030
21.4%
M 687
14.3%
c 686
14.3%
e 344
 
7.2%
i 344
 
7.2%
d 344
 
7.2%
u 343
 
7.1%
h 343
 
7.1%
P 343
 
7.1%
o 343
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1030
21.4%
M 687
14.3%
c 686
14.3%
e 344
 
7.2%
i 344
 
7.2%
d 344
 
7.2%
u 343
 
7.1%
h 343
 
7.1%
P 343
 
7.1%
o 343
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1030
21.4%
M 687
14.3%
c 686
14.3%
e 344
 
7.2%
i 344
 
7.2%
d 344
 
7.2%
u 343
 
7.1%
h 343
 
7.1%
P 343
 
7.1%
o 343
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1030
21.4%
M 687
14.3%
c 686
14.3%
e 344
 
7.2%
i 344
 
7.2%
d 344
 
7.2%
u 343
 
7.1%
h 343
 
7.1%
P 343
 
7.1%
o 343
 
7.1%

budget_categoria
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing4
Missing (%)0.4%
Memory size8.2 KiB
Muy Alto
489 
Alto
439 
Medio
100 
Bajo
 
1

Length

Max length8
Median length5
Mean length5.9980564
Min length4

Characters and Unicode

Total characters6172
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowMedio
2nd rowAlto
3rd rowMuy Alto
4th rowAlto
5th rowAlto

Common Values

ValueCountFrequency (%)
Muy Alto 489
47.3%
Alto 439
42.5%
Medio 100
 
9.7%
Bajo 1
 
0.1%
(Missing) 4
 
0.4%

Length

2025-05-25T10:32:37.227420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:37.324965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
alto 928
61.1%
muy 489
32.2%
medio 100
 
6.6%
bajo 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 1029
16.7%
A 928
15.0%
t 928
15.0%
l 928
15.0%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1029
16.7%
A 928
15.0%
t 928
15.0%
l 928
15.0%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1029
16.7%
A 928
15.0%
t 928
15.0%
l 928
15.0%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1029
16.7%
A 928
15.0%
t 928
15.0%
l 928
15.0%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

roi_recalculated
Real number (ℝ)

High correlation  Missing 

Distinct1007
Distinct (%)99.6%
Missing22
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean24.767696
Minimum-0.998254
Maximum884.759
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)4.1%
Memory size8.2 KiB
2025-05-25T10:32:37.476543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.998254
5-th percentile0.238584
Q14.414886
median9.416141
Q320.03884
95-th percentile95.139108
Maximum884.759
Range885.75725
Interquartile range (IQR)15.623954

Descriptive statistics

Standard deviation61.273527
Coefficient of variation (CV)2.4739293
Kurtosis75.095159
Mean24.767696
Median Absolute Deviation (MAD)6.558041
Skewness7.5471204
Sum25040.14
Variance3754.4451
MonotonicityNot monotonic
2025-05-25T10:32:37.600641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.795984 2
 
0.2%
4.604299 2
 
0.2%
2 2
 
0.2%
0.5 2
 
0.2%
0.209189 1
 
0.1%
6.387951 1
 
0.1%
4.968786 1
 
0.1%
17.144859 1
 
0.1%
309.138805 1
 
0.1%
3.320839 1
 
0.1%
Other values (997) 997
96.5%
(Missing) 22
 
2.1%
ValueCountFrequency (%)
-0.998254 1
0.1%
-0.995 1
0.1%
-0.959744 1
0.1%
-0.923559 1
0.1%
-0.917185 1
0.1%
-0.904294 1
0.1%
-0.886265 1
0.1%
-0.882994 1
0.1%
-0.879069 1
0.1%
-0.869787 1
0.1%
ValueCountFrequency (%)
884.758999 1
0.1%
627.007142 1
0.1%
624.893465 1
0.1%
612.621835 1
0.1%
527.274718 1
0.1%
524.426429 1
0.1%
323.705931 1
0.1%
321.970828 1
0.1%
309.138805 1
0.1%
308.543649 1
0.1%

start_month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.2%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.407767
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:37.686003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.407637
Coefficient of variation (CV)0.5317979
Kurtosis-1.1791358
Mean6.407767
Median Absolute Deviation (MAD)3
Skewness-0.0097135354
Sum6600
Variance11.61199
MonotonicityNot monotonic
2025-05-25T10:32:37.756278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 100
9.7%
9 99
9.6%
8 97
9.4%
3 96
9.3%
7 93
9.0%
4 92
8.9%
11 89
8.6%
6 83
8.0%
5 75
7.3%
12 71
6.9%
Other values (2) 135
13.1%
ValueCountFrequency (%)
1 100
9.7%
2 66
6.4%
3 96
9.3%
4 92
8.9%
5 75
7.3%
6 83
8.0%
7 93
9.0%
8 97
9.4%
9 99
9.6%
10 69
6.7%
ValueCountFrequency (%)
12 71
6.9%
11 89
8.6%
10 69
6.7%
9 99
9.6%
8 97
9.4%
7 93
9.0%
6 83
8.0%
5 75
7.3%
4 92
8.9%
3 96
9.3%

temporada_inicio
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
Verano
273 
Primavera
263 
Otoño
260 
Invierno
237 

Length

Max length9
Median length8
Mean length6.9709584
Min length5

Characters and Unicode

Total characters7201
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimavera
2nd rowInvierno
3rd rowInvierno
4th rowOtoño
5th rowVerano

Common Values

ValueCountFrequency (%)
Verano 273
26.4%
Primavera 263
25.5%
Otoño 260
25.2%
Invierno 237
22.9%

Length

2025-05-25T10:32:37.844900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:37.912887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
verano 273
26.4%
primavera 263
25.5%
otoño 260
25.2%
invierno 237
22.9%

Most occurring characters

ValueCountFrequency (%)
r 1036
14.4%
o 1030
14.3%
a 799
11.1%
e 773
10.7%
n 747
10.4%
i 500
6.9%
v 500
6.9%
V 273
 
3.8%
P 263
 
3.7%
m 263
 
3.7%
Other values (4) 1017
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1036
14.4%
o 1030
14.3%
a 799
11.1%
e 773
10.7%
n 747
10.4%
i 500
6.9%
v 500
6.9%
V 273
 
3.8%
P 263
 
3.7%
m 263
 
3.7%
Other values (4) 1017
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1036
14.4%
o 1030
14.3%
a 799
11.1%
e 773
10.7%
n 747
10.4%
i 500
6.9%
v 500
6.9%
V 273
 
3.8%
P 263
 
3.7%
m 263
 
3.7%
Other values (4) 1017
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1036
14.4%
o 1030
14.3%
a 799
11.1%
e 773
10.7%
n 747
10.4%
i 500
6.9%
v 500
6.9%
V 273
 
3.8%
P 263
 
3.7%
m 263
 
3.7%
Other values (4) 1017
14.1%

net_profit
Real number (ℝ)

High correlation 

Distinct1009
Distinct (%)98.2%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean453393.44
Minimum-9949999
Maximum987859.73
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)4.1%
Memory size8.2 KiB
2025-05-25T10:32:38.006248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-9949999
5-th percentile10945.703
Q1216214.82
median473021.14
Q3708529.39
95-th percentile895800.82
Maximum987859.73
Range10937859
Interquartile range (IQR)492314.57

Descriptive statistics

Standard deviation434467.5
Coefficient of variation (CV)0.95825712
Kurtosis319.65116
Mean453393.44
Median Absolute Deviation (MAD)248253.28
Skewness-13.390658
Sum4.6563507 × 108
Variance1.8876201 × 1011
MonotonicityNot monotonic
2025-05-25T10:32:38.111907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701511.18 4
 
0.4%
373584.32 3
 
0.3%
498896.12 3
 
0.3%
75368.98 3
 
0.3%
169440.88 3
 
0.3%
110421.1 2
 
0.2%
8219.45 2
 
0.2%
482732.83 2
 
0.2%
143918.14 2
 
0.2%
694261.88 2
 
0.2%
Other values (999) 1001
96.9%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
-9949999 1
0.1%
-92091.91 1
0.1%
-91618.85 1
0.1%
-85765.04 1
0.1%
-83260.49 1
0.1%
-77408.2 1
0.1%
-69612.81 1
0.1%
-68489.09 1
0.1%
-67005.31 1
0.1%
-61869.89 1
0.1%
ValueCountFrequency (%)
987859.73 1
0.1%
987359.82 1
0.1%
979827.4 1
0.1%
974958.97 1
0.1%
973355.11 1
0.1%
965199.63 1
0.1%
964497.81 1
0.1%
963838 1
0.1%
960202.7 1
0.1%
958135.52 1
0.1%

conversions
Real number (ℝ)

High correlation  Zeros 

Distinct997
Distinct (%)97.1%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean278799.19
Minimum0
Maximum964632.77
Zeros11
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:38.225239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15924.567
Q197706.974
median216427.45
Q3421233.97
95-th percentile693291.25
Maximum964632.77
Range964632.77
Interquartile range (IQR)323526.99

Descriptive statistics

Standard deviation220124.59
Coefficient of variation (CV)0.78954529
Kurtosis-0.15716255
Mean278799.19
Median Absolute Deviation (MAD)148290.94
Skewness0.80390105
Sum2.8632677 × 108
Variance4.8454835 × 1010
MonotonicityNot monotonic
2025-05-25T10:32:38.340707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.1%
283837.392 4
 
0.4%
340962.006 3
 
0.3%
107245.5592 3
 
0.3%
374062.4137 3
 
0.3%
17092.1587 3
 
0.3%
128303.6776 3
 
0.3%
478788.255 2
 
0.2%
93500.2371 2
 
0.2%
95954.3585 2
 
0.2%
Other values (987) 991
95.9%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
0 11
1.1%
83.3217 1
 
0.1%
419.095 1
 
0.1%
758.8377 1
 
0.1%
1374.5772 1
 
0.1%
1436.4585 1
 
0.1%
2533.2462 1
 
0.1%
2661.8471 1
 
0.1%
3568.474 1
 
0.1%
3583.176 1
 
0.1%
ValueCountFrequency (%)
964632.7746 1
0.1%
954051.8638 1
0.1%
935379.7945 1
0.1%
921771.3204 1
0.1%
915375.236 1
0.1%
907059.9884 1
0.1%
899700.0163 1
0.1%
894373.9296 1
0.1%
888263.5488 1
0.1%
886375.4536 1
0.1%

costo_por_conversion
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct996
Distinct (%)98.2%
Missing19
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean2.6190378
Minimum0.0018731819
Maximum999.9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:38.453025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0018731819
5-th percentile0.016754548
Q10.086262216
median0.18806696
Q30.50232732
95-th percentile2.382806
Maximum999.9999
Range999.99803
Interquartile range (IQR)0.4160651

Descriptive statistics

Standard deviation39.383677
Coefficient of variation (CV)15.03746
Kurtosis530.08871
Mean2.6190378
Median Absolute Deviation (MAD)0.13602286
Skewness22.68424
Sum2655.7044
Variance1551.074
MonotonicityNot monotonic
2025-05-25T10:32:38.562816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02847510662 4
 
0.4%
0.6597090713 3
 
0.3%
0.3431431593 3
 
0.3%
0.8535931743 3
 
0.3%
0.05195001111 3
 
0.3%
1.020988006 2
 
0.2%
0.003793367905 2
 
0.2%
1.006245684 2
 
0.2%
0.6674149148 2
 
0.2%
0.9855192339 2
 
0.2%
Other values (986) 988
95.6%
(Missing) 19
 
1.8%
ValueCountFrequency (%)
0.001873181857 1
0.1%
0.002138444974 1
0.1%
0.00242078181 1
0.1%
0.002565852872 1
0.1%
0.003505471279 1
0.1%
0.003793367905 2
0.2%
0.004627578835 1
0.1%
0.004681974146 1
0.1%
0.00524825398 1
0.1%
0.005307795266 1
0.1%
ValueCountFrequency (%)
999.9999 1
0.1%
743.8410402 1
0.1%
92.00362607 1
0.1%
87.92350183 1
0.1%
55.12808062 1
0.1%
39.41134107 1
0.1%
30.12062564 1
0.1%
19.34929765 1
0.1%
18.68848204 1
0.1%
17.28038749 1
0.1%

costo_clicks
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct997
Distinct (%)98.8%
Missing24
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean31044.366
Minimum-1555376.8
Maximum1012624.8
Zeros11
Zeros (%)1.1%
Negative42
Negative (%)4.1%
Memory size8.2 KiB
2025-05-25T10:32:38.680936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1555376.8
5-th percentile0
Q18940.5662
median22742.548
Q347686.748
95-th percentile97673.71
Maximum1012624.8
Range2568001.6
Interquartile range (IQR)38746.182

Descriptive statistics

Standard deviation79726.015
Coefficient of variation (CV)2.5681315
Kurtosis193.70373
Mean31044.366
Median Absolute Deviation (MAD)16814.714
Skewness-7.0431349
Sum31323765
Variance6.3562375 × 109
MonotonicityNot monotonic
2025-05-25T10:32:38.792927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.1%
23292.48365 2
 
0.2%
3270.167339 2
 
0.2%
1570.819706 1
 
0.1%
47319.19835 1
 
0.1%
5914.933809 1
 
0.1%
90000 1
 
0.1%
183968.5332 1
 
0.1%
20142.20165 1
 
0.1%
1548.78083 1
 
0.1%
Other values (987) 987
95.5%
(Missing) 24
 
2.3%
ValueCountFrequency (%)
-1555376.751 1
0.1%
-816538.0608 1
0.1%
-489639.1438 1
0.1%
-329231.9765 1
0.1%
-234386.3557 1
0.1%
-86746.69251 1
0.1%
-63437.61655 1
0.1%
-57373.73523 1
0.1%
-57115.54664 1
0.1%
-52963.07883 1
0.1%
ValueCountFrequency (%)
1012624.836 1
0.1%
471805.9178 1
0.1%
439060.1711 1
0.1%
281651.6023 1
0.1%
275238.1825 1
0.1%
274255.1611 1
0.1%
265316.3919 1
0.1%
250937.3274 1
0.1%
234409.6791 1
0.1%
203779.3986 1
0.1%

ingresos_por_click
Real number (ℝ)

High correlation  Missing 

Distinct996
Distinct (%)99.8%
Missing35
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean64.591694
Minimum-8.86265
Maximum3219.7083
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)4.1%
Memory size8.2 KiB
2025-05-25T10:32:38.906926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-8.86265
5-th percentile0.40099664
Q17.7735117
median19.734866
Q347.310948
95-th percentile219.85865
Maximum3219.7083
Range3228.5709
Interquartile range (IQR)39.537437

Descriptive statistics

Standard deviation193.18982
Coefficient of variation (CV)2.990939
Kurtosis106.76481
Mean64.591694
Median Absolute Deviation (MAD)14.546378
Skewness8.9297946
Sum64462.51
Variance37322.306
MonotonicityNot monotonic
2025-05-25T10:32:39.022826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.854421154 2
 
0.2%
216.98996 2
 
0.2%
-3.554607407 1
 
0.1%
17.81346111 1
 
0.1%
12.10579891 1
 
0.1%
13.46013714 1
 
0.1%
15.76300714 1
 
0.1%
29.22815294 1
 
0.1%
36.4784234 1
 
0.1%
363.6927118 1
 
0.1%
Other values (986) 986
95.5%
(Missing) 35
 
3.4%
ValueCountFrequency (%)
-8.86265 1
0.1%
-6.347118182 1
0.1%
-5.86046 1
0.1%
-4.975 1
0.1%
-4.772427778 1
0.1%
-3.769472222 1
0.1%
-3.554607407 1
0.1%
-3.536277778 1
0.1%
-3.028668 1
0.1%
-2.0889 1
0.1%
ValueCountFrequency (%)
3219.70828 1
0.1%
2090.023807 1
0.1%
2010.815907 1
0.1%
1904.152535 1
0.1%
1256.362453 1
0.1%
1225.060608 1
0.1%
1141.698592 1
0.1%
984.483895 1
0.1%
976.434663 1
0.1%
946.8082803 1
0.1%

revenue_per_dollar
Real number (ℝ)

High correlation 

Distinct1007
Distinct (%)98.1%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean25.962728
Minimum0.0017459393
Maximum885.759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T10:32:39.133138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0017459393
5-th percentile1.2378606
Q15.4136417
median10.394971
Q321.100119
95-th percentile96.437366
Maximum885.759
Range885.75725
Interquartile range (IQR)15.686478

Descriptive statistics

Standard deviation61.534323
Coefficient of variation (CV)2.3701024
Kurtosis72.929897
Mean25.962728
Median Absolute Deviation (MAD)6.5434321
Skewness7.4236115
Sum26663.721
Variance3786.4729
MonotonicityNot monotonic
2025-05-25T10:32:39.244180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.79598382 4
 
0.4%
29.16556672 3
 
0.3%
5.41364175 3
 
0.3%
6.165885639 3
 
0.3%
5.604299171 3
 
0.3%
1.209189426 2
 
0.2%
7.387950633 2
 
0.2%
5.968785528 2
 
0.2%
18.14485942 2
 
0.2%
310.1388048 2
 
0.2%
Other values (997) 1001
96.9%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
0.001745939291 1
0.1%
0.0050000005 1
0.1%
0.04025606231 1
0.1%
0.07644127681 1
0.1%
0.08281497847 1
0.1%
0.09570641194 1
0.1%
0.1137352334 1
0.1%
0.1170059864 1
0.1%
0.1209305057 1
0.1%
0.1302132181 1
0.1%
ValueCountFrequency (%)
885.7589994 1
0.1%
628.0071416 1
0.1%
625.8934652 1
0.1%
613.6218345 1
0.1%
528.2747182 1
0.1%
525.4264286 1
0.1%
324.7059312 1
0.1%
322.9708283 1
0.1%
310.1388048 2
0.2%
309.5436491 1
0.1%

efficiency_index
Real number (ℝ)

High correlation  Zeros 

Distinct756
Distinct (%)73.6%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.29270312
Minimum-0.02
Maximum1.2
Zeros17
Zeros (%)1.6%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T10:32:39.353052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.0396
Q10.11365
median0.234
Q30.42415
95-th percentile0.7287
Maximum1.2
Range1.22
Interquartile range (IQR)0.3105

Descriptive statistics

Standard deviation0.21882539
Coefficient of variation (CV)0.74760186
Kurtosis0.032128692
Mean0.29270312
Median Absolute Deviation (MAD)0.138
Skewness0.88382186
Sum300.6061
Variance0.047884553
MonotonicityNot monotonic
2025-05-25T10:32:39.464632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
1.6%
0.14 6
 
0.6%
0.204 6
 
0.6%
0.36 6
 
0.6%
0.096 6
 
0.6%
0.112 5
 
0.5%
0.168 5
 
0.5%
0.252 4
 
0.4%
0.126 4
 
0.4%
0.078 4
 
0.4%
Other values (746) 964
93.3%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
-0.02 1
 
0.1%
0 17
1.6%
0.016 1
 
0.1%
0.0168 1
 
0.1%
0.017 1
 
0.1%
0.019 2
 
0.2%
0.02 2
 
0.2%
0.0208 1
 
0.1%
0.0216 1
 
0.1%
0.0247 1
 
0.1%
ValueCountFrequency (%)
1.2 1
0.1%
0.9603 1
0.1%
0.9506 1
0.1%
0.891 1
0.1%
0.8835 1
0.1%
0.882 2
0.2%
0.8736 1
0.1%
0.873 1
0.1%
0.8712 1
0.1%
0.864 1
0.1%

theme
Categorical

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
Procesos/Metodología
282 
Arquitectura/Infraestructura
139 
Usuario/Cliente
131 
Red/Conectividad
97 
Tecnología/IT
78 
Other values (5)
306 

Length

Max length28
Median length20
Mean length18.524685
Min length9

Characters and Unicode

Total characters19136
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUsuario/Cliente
2nd rowProcesos/Metodología
3rd rowProductos/Servicios
4th rowProcesos/Metodología
5th rowGestión/Management

Common Values

ValueCountFrequency (%)
Procesos/Metodología 282
27.3%
Arquitectura/Infraestructura 139
13.5%
Usuario/Cliente 131
12.7%
Red/Conectividad 97
 
9.4%
Tecnología/IT 78
 
7.6%
Gestión/Management 75
 
7.3%
Productos/Servicios 72
 
7.0%
Seguridad 57
 
5.5%
Datos/Análisis 56
 
5.4%
Innovación/Generacional 46
 
4.5%

Length

2025-05-25T10:32:39.577340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T10:32:39.671973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
procesos/metodología 282
27.3%
arquitectura/infraestructura 139
13.5%
usuario/cliente 131
12.7%
red/conectividad 97
 
9.4%
tecnología/it 78
 
7.6%
gestión/management 75
 
7.3%
productos/servicios 72
 
7.0%
seguridad 57
 
5.5%
datos/análisis 56
 
5.4%
innovación/generacional 46
 
4.5%

Most occurring characters

ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Interactions

2025-05-25T10:32:31.970861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:13.521345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.814618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.010932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.418140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.597321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.922665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.107247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.032165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.321502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.592240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.766347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.383682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.686432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.063642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:13.611111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.903404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.117967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.498306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.689284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.011263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.268348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.125656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.399213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.672796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.857198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.517684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.775134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.154382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:13.698469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.999526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.232174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.575710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.780786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.090709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.352731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.211819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.480252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.752948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.937735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.603915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.858465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.246180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:13.788031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.081321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.340997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.661667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.884850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.178345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.451459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.304675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.567701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.837023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.039382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.695690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.951567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.335960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:13.909666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.160453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.433199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.741669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.981201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.262413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.531092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.388870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.651341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.916522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.127639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.784202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.034716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.431524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.012935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.246560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.527273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.830650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.072268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.346438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.632250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.477891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.743195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.023878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.216423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.874693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.118469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.519004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.085752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.324579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.608483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.912193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.208918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.421362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.712014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.566832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.822991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.110796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.301673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.961382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.208360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.601730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.168281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.404770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.700073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.997185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.296628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.500340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.799632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.667395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.910268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.188968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.385477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.044737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.321240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.690527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.251184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.487216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.799277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.084717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.384185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.581324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:23.499205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.752394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.993802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.269891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.836227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.134959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.426767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.773922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.334213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.569528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.893797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.171189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.470596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.668280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:23.582933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.836837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.076816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.351299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:28.922723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.226250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.517612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.857594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.429820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.654645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:16.981945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.253604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.556729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.754045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:23.671239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:24.982862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.156515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.427429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.016362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.319384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.596596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:32.948237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.514070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.740149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.073689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.339815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.649219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.838075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:23.762514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.064513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.244506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.515323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.100457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.411076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.727551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:33.052853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.628007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.829603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.165149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.432197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.742241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:20.926461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:23.852768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.151504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.369795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.599021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.193855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.506763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.811203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:33.137058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:14.721185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:15.907423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:17.332997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:18.511077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:19.832396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:21.001565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:23.933782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:25.233754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:26.478143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:27.682428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:29.285114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:30.595293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T10:32:31.884390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-25T10:32:39.807133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
budgetbudget_categoriachannelconversion_categoriaconversion_rateconversionscosto_clickscosto_por_conversionduracion_diasefficiency_indexingresos_por_clicknet_profitrevenuerevenue_categoriarevenue_per_dollarroiroi_categoriaroi_recalculatedstart_monthtarget_audiencetemporada_iniciothemetype
budget1.0000.0000.0000.000-0.015-0.0220.6400.592-0.0550.011-0.612-0.114-0.0190.003-0.6720.0270.000-0.675-0.0580.0000.0000.0000.000
budget_categoria0.0001.0000.0000.0000.0580.0320.0000.0000.0290.0000.3490.0000.0000.0000.4810.5740.2640.4830.0510.0000.0130.0470.000
channel0.0000.0001.0000.0290.0160.0190.0000.0000.0000.0360.0000.0000.0000.0530.0000.0560.0180.0000.0870.0000.0720.0000.000
conversion_categoria0.0000.0000.0291.0000.9060.4660.0490.0000.0000.5020.0630.0000.0000.0000.0000.0000.0330.0000.0480.0000.0000.0000.018
conversion_rate-0.0150.0580.0160.9061.0000.6100.485-0.434-0.0320.703-0.3610.0110.0110.0000.0200.0350.0180.015-0.0740.0000.0000.0000.000
conversions-0.0220.0320.0190.4660.6101.0000.358-0.7590.0010.4770.2400.7270.7300.5560.5320.0540.0820.527-0.0680.0300.0000.0000.000
costo_clicks0.6400.0000.0000.0490.4850.3581.0000.173-0.0710.377-0.632-0.0550.0090.152-0.4590.0180.000-0.459-0.1100.0000.0690.0000.000
costo_por_conversion0.5920.0000.0000.000-0.434-0.7590.1731.000-0.039-0.323-0.600-0.647-0.5930.003-0.882-0.0280.000-0.8790.0160.0000.0090.0000.000
duracion_dias-0.0550.0290.0000.000-0.0320.001-0.071-0.0391.000-0.0360.0630.0380.0370.0000.057-0.0120.0730.0510.3520.0600.3550.0460.041
efficiency_index0.0110.0000.0360.5020.7030.4770.377-0.323-0.0361.000-0.2630.0320.0320.0220.0230.6820.4240.015-0.0880.0540.0540.0000.000
ingresos_por_click-0.6120.3490.0000.063-0.3610.240-0.632-0.6000.063-0.2631.0000.6600.6060.0590.896-0.0070.0000.8960.0450.0170.0000.0270.000
net_profit-0.1140.0000.0000.0000.0110.727-0.055-0.6470.0380.0320.6601.0000.9950.0030.7390.0380.0000.736-0.0240.0000.0000.0000.000
revenue-0.0190.0000.0000.0000.0110.7300.009-0.5930.0370.0320.6060.9951.0000.9440.6770.0410.0770.675-0.0350.0000.0000.0330.000
revenue_categoria0.0030.0000.0530.0000.0000.5560.1520.0030.0000.0220.0590.0030.9441.0000.0990.0640.0730.0980.0190.0000.0240.0000.000
revenue_per_dollar-0.6720.4810.0000.0000.0200.532-0.459-0.8820.0570.0230.8960.7390.6770.0991.0000.0170.0331.0000.0180.0000.0240.0000.000
roi0.0270.5740.0560.0000.0350.0540.018-0.028-0.0120.682-0.0070.0380.0410.0640.0171.0000.9840.012-0.0570.0850.0660.0350.039
roi_categoria0.0000.2640.0180.0330.0180.0820.0000.0000.0730.4240.0000.0000.0770.0730.0330.9841.0000.0310.0420.0000.0350.0000.000
roi_recalculated-0.6750.4830.0000.0000.0150.527-0.459-0.8790.0510.0150.8960.7360.6750.0981.0000.0120.0311.0000.0220.0000.0000.0000.000
start_month-0.0580.0510.0870.048-0.074-0.068-0.1100.0160.352-0.0880.045-0.024-0.0350.0190.018-0.0570.0420.0221.0000.0000.9420.0000.023
target_audience0.0000.0000.0000.0000.0000.0300.0000.0000.0600.0540.0170.0000.0000.0000.0000.0850.0000.0000.0001.0000.0430.0440.706
temporada_inicio0.0000.0130.0720.0000.0000.0000.0690.0090.3550.0540.0000.0000.0000.0240.0240.0660.0350.0000.9420.0431.0000.0000.056
theme0.0000.0470.0000.0000.0000.0000.0000.0000.0460.0000.0270.0000.0330.0000.0000.0350.0000.0000.0000.0440.0001.0000.000
type0.0000.0000.0000.0180.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0390.0000.0000.0230.7060.0560.0001.000

Missing values

2025-05-25T10:32:33.293241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T10:32:33.483845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-25T10:32:33.751779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme
0Public-key multi-tasking throughput8082.300.35emailB2Borganic0.40709593.48328.0BajoBajaMuchaMedio86.7959844.0Primavera701511.18283837.39200.0284753270.167339216.98996087.7959840.1400Usuario/Cliente
1De-engineered analyzing task-force17712.980.74emailB2Cpromotion0.66516609.10432.0MedioMediaMediaAlto28.1655672.0Invierno498896.12340962.00600.05195012105.63259742.67510229.1655670.4884Procesos/Metodología
2Balanced solution-oriented Local Area Network84643.100.37podcastB2Bpaid0.28458227.42295.0BajoBajaMediaMuy Alto4.41364212.0Invierno373584.32128303.67760.65970929069.79714315.7630075.4136420.1036Productos/Servicios
3Distributed real-time methodology14589.750.47webinarB2Borganic0.1989958.73366.0BajoBajaPocaAlto5.1658869.0Otoño75368.9817092.15870.8535933308.65967627.1888746.1658860.0893Procesos/Metodología
4Front-line executive infrastructure39291.900.30social mediaB2Bpromotion0.8147511.35313.0BajoAltaPocaAlto0.2091897.0Verano8219.4538484.19351.020988183968.5332400.2582581.2091890.2430Gestión/Management
5Upgradable transitional data-warehouse75569.280.59social mediaB2Creferral0.67558302.11167.0MedioMediaMediaMuy Alto6.3879516.0Verano482732.83374062.41370.20202358557.4957769.5342557.3879510.3953Datos/Análisis
6Innovative context-sensitive framework28964.450.59emailB2Creferral0.17172882.59359.0MedioBajaPocaAlto4.9687863.0Primavera143918.1429390.04030.9855195914.93380929.2281535.9687860.1003Innovación/Generacional
7User-friendly client-driven service-desk36800.580.40webinarB2Cpromotion0.52206241.46339.0BajoMediaPocaAlto4.6042991.0Invierno169440.88107245.55920.34314323292.4836558.8544215.6042990.2080Usuario/Cliente
8Proactive neutral methodology40493.880.16webinarB2Corganic0.47734755.76492.0BajoMediaMuchaAlto17.1448599.0Otoño694261.88345335.20720.11726020142.20164836.47842318.1448590.0752Procesos/Metodología
9Intuitive responsive support1816.220.81social mediaB2Creferral0.85563280.30496.0MedioAltaMediaMedio309.13880511.0Otoño561464.08478788.25500.0037931548.780830363.692712310.1388050.6885Usuario/Cliente
campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme
1023Broken-date campaign25000.00.45emailB2BorganicNaN87500.0NaNBajoNaNPocaAlto2.500000NaNOtoño62500.0NaNNaNNaNNaN3.500000NaNProcesos/Metodología
1024Negative ROI test-10000.0-0.20podcastB2Creferral0.10NaN207.0PérdidaBajaNaNBajoNaN10.0OtoñoNaNNaNNaNNaNNaNNaN-0.020Datos/Análisis
1025Null-heavy campaignNaNNaNB2Bsocial mediaNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0InviernoNaNNaNNaNNaNNaNNaNNaNGestión/Management
1026Future campaign75000.00.90webinarB2Cpromotion0.65200000.0151.0MedioMediaPocaMuy Alto1.6666671.0Invierno125000.0130000.00.57692377999.98442.5641032.6666670.585Innovación/Generacional
1027Extra long name campaign test30000.00.25emailNaNpaid0.4045000.0153.0BajoBajaPocaAlto0.5000004.0Primavera15000.018000.01.66666736000.00001.2500001.5000000.100Procesos/Metodología
1028No revenue campaign20000.00.30social mediaB2Borganic0.50NaN181.0BajoMediaNaNAltoNaN2.0InviernoNaNNaNNaNNaNNaNNaN0.150Datos/Análisis
1029Random mess100000.0NaNpodcastNaNreferralNaN300000.0NaNNaNNaNPocaMuy Alto2.0000006.0Verano200000.0NaNNaNNaNNaN3.000000NaNRed/Conectividad
1030Invalid budgetNaNNaNemailB2Cpromotion0.2050000.0182.0NaNBajaPocaNaNNaN12.0InviernoNaN10000.0NaNNaNNaNNaNNaNRed/Conectividad
1031Overlapping dates60000.00.60webinarB2Bpaid0.7090000.0-60.0MedioMediaPocaMuy Alto0.5000003.0Primavera30000.063000.00.952381126000.00000.7142861.5000000.420Arquitectura/Infraestructura
1032Too many conversions40000.00.80social mediaB2Corganic1.50120000.0184.0MedioAltaPocaAlto2.0000005.0Primavera80000.0180000.00.22222290000.00001.3333333.0000001.200Procesos/Metodología

Duplicate rows

Most frequently occurring

campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme# duplicates
0Balanced solution-oriented Local Area Network84643.100.37podcastB2Bpaid0.28458227.42295.0BajoBajaMediaMuy AltoNaN12.0Invierno373584.32128303.67760.659709NaNNaN5.4136420.1036Productos/Servicios2
1De-engineered analyzing task-force17712.980.74emailB2Cpromotion0.66516609.10432.0MedioMediaMediaAltoNaN2.0Invierno498896.12340962.00600.051950NaNNaN29.1655670.4884Procesos/Metodología2
2Distributed real-time methodology14589.750.47webinarB2Borganic0.1989958.73366.0BajoBajaPocaAltoNaN9.0Otoño75368.9817092.15870.853593NaNNaN6.1658860.0893Procesos/Metodología2
3Public-key multi-tasking throughput8082.300.35emailB2Borganic0.40709593.48328.0BajoBajaMuchaMedioNaN4.0Primavera701511.18283837.39200.028475NaNNaN87.7959840.1400Usuario/Cliente2